# A Quantum Extension of Variational Bayes Inference

arXiv: https://arxiv.org/abs/1712.04709 · HTML: https://mlsanity.com/paper/1712.04709/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2017.

## Built on by (most cited first, 5 of 5)

- [Variational inference with a quantum computer](https://mlsanity.com/paper/2103.06720/) (2021)
- [Quantum Speedup of Natural Gradient for Variational Bayes](https://mlsanity.com/paper/2106.05807/) (2021)
- [An Introduction to Quantum Computing for Statisticians and Data Scientists](https://mlsanity.com/paper/2112.06587/) (2021)
- [Quantum Advantage in Variational Bayes Inference](https://mlsanity.com/paper/2207.03104/) (2022)
- [Towards unsupervised representation learning for quantum data: quantum models with inference and generation](https://mlsanity.com/paper/2609.00372/) (2026)

## Abstract (arXiv)

Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry. However, VB is known to suffer from the problem of local optima. In this Letter, we generalize VB by using quantum mechanics, and propose a new algorithm, which we call quantum annealing variational Bayes (QAVB) inference. We then show that QAVB drastically improve the performance of VB by applying them to a clustering problem described by a Gaussian mixture model. Finally, we discuss an intuitive understanding on how QAVB works well.